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* fix_torch_device_generate_test * remove @ * up * correct some bugs * correct model * finish speech2text extension * up * up * up * up * Update utils/custom_init_isort.py * up * up * update with tokenizer * correct old tok * correct old tok * fix bug * up * up * add more tests * up * fix docs * up * fix some more tests * add better config * correct some more things " * fix tests * improve docs * Apply suggestions from code review * Apply suggestions from code review * final fixes * finalize * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * apply suggestions Lysandre and Sylvain * apply nicos suggestions * upload everything * finish Co-authored-by: Patrick von Platen <patrick@huggingface.co> Co-authored-by: your_github_username <your_github_email> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
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41 lines
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..
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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Speech Encoder Decoder Models
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-----------------------------------------------------------------------------------------------------------------------
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The :class:`~transformers.SpeechEncoderDecoderModel` can be used to initialize a speech-sequence-to-text-sequence model
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with any pretrained speech autoencoding model as the encoder (*e.g.* :doc:`Wav2Vec2 <wav2vec2>`, :doc:`Hubert
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<hubert>`) and any pretrained autoregressive model as the decoder.
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The effectiveness of initializing speech-sequence-to-text-sequence models with pretrained checkpoints for speech
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recognition and speech translation has *e.g.* been shown in `Large-Scale Self- and Semi-Supervised Learning for Speech
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Translation <https://arxiv.org/abs/2104.06678>`__ by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli,
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Alexis Conneau.
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An example of how to use a :class:`~transformers.SpeechEncoderDecoderModel` for inference can be seen in
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:doc:`Speech2Text2 <speech_to_text_2>`.
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SpeechEncoderDecoderConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SpeechEncoderDecoderConfig
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:members:
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SpeechEncoderDecoderModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SpeechEncoderDecoderModel
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:members: forward, from_encoder_decoder_pretrained
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